{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# @author: tongzi\n",
    "# @created date: 2019/10/09\n",
    "# @description: featur engineering\n",
    "# @last modification: 2019/10/9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "支持向量机（support vector machine，SVM）是非常强大、灵活的有监督学习算法，既可\n",
    "用于分类，也可用于回归。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb1de4e0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaf4e6d8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "X, y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.6)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个线性判别分类器尝试画一条将数据分成两部分的直线，这样就构成了一个分类模型。对于上图的二维数据来说，这个任务其实可以手动完成。但是我们马上发现一个问题：在这两种类型之间，有不止一条直线可以将它们完美分割。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb23a550>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfit = np.linspace(-1, 3.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)\n",
    "\n",
    "for m, b in [(1, 0.65), (0.5, 1.6), (-0.2, 2.9)]:\n",
    "    plt.plot(xfit, m * xfit + b, '-k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "虽然这三个不同的分割器都能完美地判别这些样本，但是选择不同的分割线，可能会让  新的数据点（例如图 5-54中的“X”点）分配到不同的标签。显然，“画一条分割不同类型的直线”还不够。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5.7.2支持向量机：边界最大化  \n",
    "支持向量机提供了改进这个问题的方法，它直观的解释是：不再画一条细线来区分类\n",
    "型，而是画一条到最近点边界、有宽度的线条。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb214c88>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xfit = np.linspace(-1, 3.5)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plt.plot([0.6], [2.1], 'x', color='red', markeredgewidth=2, markersize=10)\n",
    "\n",
    "for m, b, d in [(1, 0.65, 0.33), (0.5, 1.6, 0.55), (-0.2, 2.9, 0.2)]:\n",
    "    yfit = m * xfit + b\n",
    "    plt.plot(xfit, yfit, '-k')\n",
    "    plt.fill_between(xfit, yfit - d, yfit + d, edgecolor='none', \n",
    "                     color='#AAAAAA', alpha=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在支持向量机中，选择边界最大的那条线是模型最优解。支持向量机其实就是一个**边界最大化**评估器。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1.拟合支持向量机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10000000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC # support vector classifier\n",
    "model = SVC(kernel='linear', C=1E10)\n",
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svc_decision_function(model, ax=None, plot_support=True):\n",
    "  if ax is None:\n",
    "    ax = plt.gca()\n",
    "  \n",
    "  xlim = ax.get_xlim()\n",
    "  ylim = ax.get_ylim()\n",
    "\n",
    "  # 创建评估模型的网格\n",
    "  x = np.linspace(xlim[0], xlim[1], 30)\n",
    "  y = np.linspace(ylim[0], ylim[1], 30)\n",
    "  Y, X = np.meshgrid(y, x)\n",
    "  xy = np.vstack([X.ravel(), Y.ravel()]).T # xy.shape=(900, 2)\n",
    "  P = model.decision_function(xy).reshape(X.shape)\n",
    "\n",
    "  # 画出决策边界\n",
    "  ax.contour(X, Y, P, colors='k', levels=[-1, 0, 1], alpha=0.5, \n",
    "             linestyles=['--', '-', '--'])\n",
    "  # 画支持向量\n",
    "  if plot_support:\n",
    "    ax.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1],\n",
    "              s=300, linewidth=1, facecolor='none', color='k')\n",
    "\n",
    "  ax.set_xlim(xlim)  \n",
    "  ax.set_ylim(ylim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb5d7940>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn', alpha=0.8)\n",
    "plot_svc_decision_function(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这就是两类数据间隔最大的分割线。你会发现有一些点正好就在边界线上，在图 5-56 中用黑圆圈表示。这些点是拟合的关键支持点，被称为**支持向量**，支持向量机算法也因此得名。在 Scikit-Learn 里面，支持向量的坐标存放在分类器的 support_vectors_ 属性中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.44359863, 3.11530945],\n",
       "       [2.33812285, 3.43116792],\n",
       "       [2.06156753, 1.96918596]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.support_vectors_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**分类器能够成功拟合的关键因素，就是这些支持向量的位置——任何在正确分类一侧远离边界线的点都不会影响拟合结果！从技术角度解释的话，是因为这些点不会对拟合模型的损失函数产生任何影响，所以只要它们没有跨越边界线，它们的位置和数量就都无关紧要。**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_svm(N=10, ax=None):\n",
    "    # 创建数据集\n",
    "    X, y = make_blobs(n_samples=N, centers=2,\n",
    "    random_state=0, cluster_std=0.60)\n",
    "    X = X[:N]\n",
    "    y = y[:N]\n",
    "    \n",
    "    # 拟合支持向量机\n",
    "    model = SVC(kernel='linear', C=1E10)\n",
    "    model.fit(X, y)\n",
    "    \n",
    "    # 可视化数据集\n",
    "    ax = ax or plt.gca()\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "    ax.set_xlim(-1, 4)\n",
    "    ax.set_ylim(-1, 6)\n",
    "    \n",
    "    # 画出决策边界\n",
    "    plot_svc_decision_function(model, ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6491d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(16, 6))\n",
    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
    "for axi, N in zip(ax, [60, 120]):\n",
    "    plot_svm(N, axi)\n",
    "    axi.set_title('N={}'.format(N))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们在左图中看到的是前 60 个训练样本的模型和支持向量。在右图中，虽然我们画了前120 个训练样本的支持向量，但是模型并没有改变：左图中的 3 个支持向量仍然适用于右图。这种对远离边界的数据点不敏感的特点正是 SVM 模型的优点之一。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04f90ed9138e4476a8b7ce8787d519c3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='N', options=(10, 20, 60, 200), value=10), Output()), _dom_classes=…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from ipywidgets import interact, fixed\n",
    "interact(plot_svm, N=[10, 20, 60, 200], ax=fixed(None));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 2超越线性边界：核函数SVM模型  \n",
    "将 SVM 模型与核函数组合使用，功能会非常强大。我们在 5.6 节介绍基函数回归时介绍过一些核函数。那时，我们将数据投影到多项式和高斯基函数定义的高维空间中，从而实现用线性分类器拟合非线性关系。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 SVM 模型中，我们可以沿用同样的思路。为了应用核函数，引入一些非线性可分的数\n",
    "据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb90e390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets.samples_generator import make_circles\n",
    "X, y = make_circles(100, factor=.1, noise=.1)\n",
    "clf = SVC(kernel='linear').fit(X, y)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plot_svc_decision_function(clf, plot_support=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "显然，这里需要用非线性判别方法来分割数据。回顾一下 5.6 节介绍的基函数回归方法，想想如何将数据投影到高维空间，从而使线性分割器可以派上用场。例如，一种简单的投影方法就是计算一个以数据圆圈（middle clump）为中心的**径向基函数**："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = np.exp(-(X**2).sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits import mplot3d\n",
    "def plot_3D(elev=30, azim=30, X=X, y=y):\n",
    "    ax = plt.subplot(projection='3d')\n",
    "    ax.scatter3D(X[:, 0], X[:, 1], r, c=y, s=50, cmap='autumn')\n",
    "    ax.view_init(elev=elev, azim=azim)\n",
    "    ax.set_xlabel('x')\n",
    "    ax.set_ylabel('y')\n",
    "    ax.set_zlabel('r')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5bb587266854bf89f5a7f0ed71b4be7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='elev', options=(-90, 90), value=-90), IntSlider(value=30, descript…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interact(plot_3D, elev=[-90, 90], azim=(-180, 180), X=fixed(X), y=fixed(y));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb63df60>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_3D()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "增加新维度之后，数据变成了线性可分状态。如果现在画一个分割平面，例如 r = 0.7，即可将数据分割。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们还需要仔细选择和优化投影方式；如果不能将径向基函数集中到正确的位置，那么就得不到如此干净、可分割的结果。通常，选择基函数比较困难，我们需要让模型自动指出最合适的基函数。  \n",
    "\n",
    "一种策略是计算基函数在数据集上每个点的变换结果，让 SVM 算法从所有结果中筛选出\n",
    "最优解。这种基函数变换方式被称为**核变换**，是基于每对数据点之间的相似度（或者核函数）计算的。  \n",
    "这种策略的问题是，如果将 N 个数据点投影到 N 维空间，当 N 不断增大的时候就会出现维度灾难，计算量巨大。但由于**核函数技巧**(http://bit.ly/2fStZeA)提供的小程序可以隐式计算核变换数据的拟合，也就是说，不需要建立完全的 N 维核函数投影空间！这个核函数技巧内置在 SVM 模型中，是使 SVM 方法如此强大的充分条件."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在 Scikit-Learn 里面，我们可以应用核函数化的 SVM 模型将线性核转变为 **RBF（径向基函数:Radius Basis Function）核**，设置 kernel 模型超参数即可:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb7624e0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb90e630>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clf = SVC(kernel='rbf', C=1E6)\n",
    "clf.fit(X, y)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "plot_svc_decision_function(clf)\n",
    "plt.scatter(clf.support_vectors_[:, 0], clf.support_vectors_[:, 1], s=100, \n",
    "            lw=1, facecolor='none')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过使用这个核函数化的支持向量机，我们找到了一条合适的非线性决策边界。在机器学习中，核变换策略经常用于将快速线性方法变换成快速非线性方法，尤其是对于那些可以应用核函数技巧的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3.SVM优化：边界软化  \n",
    "到目前为止，我们介绍的模型都是在处理非常干净的数据集，里面都有非常完美的决策\n",
    "边界。但如果你的数据有一些重叠该怎么办呢？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xb7b8240>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb2bbf98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_blobs(n_samples=100, centers=2, random_state=0, cluster_std=1.3)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了解决这个问题，SVM 实现了一些修正因子来“软化”边界。为了取得更好的拟合效\n",
    "果，它允许一些点位于边界线之内。边界线的硬度可以通过超参数进行控制，通常是 C。如果 C 很大，边界就会很硬，数据点便不能在边界内“生存”；如果 C 比较小，边界线比较软，有一些数据点就可以穿越边界线。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb7db908>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = make_blobs(n_samples=100, centers=2, random_state=0, cluster_std=0.8)\n",
    "fig, axes = plt.subplots(1, 3, figsize=(16, 6))\n",
    "fig.subplots_adjust(left=0.0625, right=0.95, wspace=0.1)\n",
    "\n",
    "for ax, C in zip(axes, [10.0, 1, 0.1]):\n",
    "    model = SVC(kernel='linear', C=C).fit(X, y)\n",
    "    ax.scatter(X[:, 0], X[:, 1], c=y, s=50, cmap='autumn')\n",
    "    plot_svc_decision_function(model, ax)\n",
    "    ax.scatter(model.support_vectors_[:, 0], model.support_vectors_[:, 1],\n",
    "              s=100, lw=1, color='k')\n",
    "    ax.set_title('C={0:.1f}'.format(C), size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参数 C 的最优值视数据集的具体情况而定，通过交叉检验或者类似的程序进行计算（详情请参见 5.3 节）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.7.3人脸识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_lfw_people"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976012\n",
      "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976009\n",
      "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976006\n",
      "Downloading LFW data (~200MB): https://ndownloader.figshare.com/files/5976015\n"
     ]
    },
    {
     "ename": "ConnectionResetError",
     "evalue": "[WinError 10054] An existing connection was forcibly closed by the remote host",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mConnectionResetError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-029e68bddc54>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 该数据集下载失败，后续的工作暂时无法展开\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mfaces\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfetch_lfw_people\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin_faces_per_person\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m60\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py\u001b[0m in \u001b[0;36mfetch_lfw_people\u001b[1;34m(data_home, funneled, resize, min_faces_per_person, color, slice_, download_if_missing, return_X_y)\u001b[0m\n\u001b[0;32m    323\u001b[0m     lfw_home, data_folder_path = _check_fetch_lfw(\n\u001b[0;32m    324\u001b[0m         \u001b[0mdata_home\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata_home\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunneled\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfunneled\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 325\u001b[1;33m         download_if_missing=download_if_missing)\n\u001b[0m\u001b[0;32m    326\u001b[0m     \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Loading LFW people faces from %s'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlfw_home\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\sklearn\\datasets\\lfw.py\u001b[0m in \u001b[0;36m_check_fetch_lfw\u001b[1;34m(data_home, funneled, download_if_missing)\u001b[0m\n\u001b[0;32m    126\u001b[0m                 logger.info(\"Downloading LFW data (~200MB): %s\",\n\u001b[0;32m    127\u001b[0m                             archive.url)\n\u001b[1;32m--> 128\u001b[1;33m                 \u001b[0m_fetch_remote\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marchive\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdirname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlfw_home\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    129\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    130\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%s is missing\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0marchive_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\sklearn\\datasets\\base.py\u001b[0m in \u001b[0;36m_fetch_remote\u001b[1;34m(remote, dirname)\u001b[0m\n\u001b[0;32m    912\u001b[0m     file_path = (remote.filename if dirname is None\n\u001b[0;32m    913\u001b[0m                  else join(dirname, remote.filename))\n\u001b[1;32m--> 914\u001b[1;33m     \u001b[0murlretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremote\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    915\u001b[0m     \u001b[0mchecksum\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_sha256\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mremote\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchecksum\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mchecksum\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[1;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[0;32m    274\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    275\u001b[0m             \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 276\u001b[1;33m                 \u001b[0mblock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    277\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m                     \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, amt)\u001b[0m\n\u001b[0;32m    445\u001b[0m             \u001b[1;31m# Amount is given, implement using readinto\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    446\u001b[0m             \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbytearray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mamt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 447\u001b[1;33m             \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    448\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mmemoryview\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtobytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    449\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\http\\client.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    489\u001b[0m         \u001b[1;31m# connection, and the user is reading more bytes than will be provided\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    490\u001b[0m         \u001b[1;31m# (for example, reading in 1k chunks)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m         \u001b[0mn\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadinto\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    492\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mn\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    493\u001b[0m             \u001b[1;31m# Ideally, we would raise IncompleteRead if the content-length\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[1;34m(self, b)\u001b[0m\n\u001b[0;32m    587\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 589\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    590\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    591\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mrecv_into\u001b[1;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[0;32m   1050\u001b[0m                   \u001b[1;34m\"non-zero flags not allowed in calls to recv_into() on %s\"\u001b[0m \u001b[1;33m%\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1051\u001b[0m                   self.__class__)\n\u001b[1;32m-> 1052\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1053\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1054\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnbytes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\ssl.py\u001b[0m in \u001b[0;36mread\u001b[1;34m(self, len, buffer)\u001b[0m\n\u001b[0;32m    909\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    910\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mbuffer\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 911\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbuffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    912\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    913\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mConnectionResetError\u001b[0m: [WinError 10054] An existing connection was forcibly closed by the remote host"
     ]
    }
   ],
   "source": [
    "# 该数据集下载失败，后续的工作暂时无法展开\n",
    "faces = fetch_lfw_people(min_faces_per_person=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(4, 5)\n",
    "for i, ax in enumerate(axes.flat):\n",
    "    ax.imshow(faces[i], cmap='bone')\n",
    "    ax.set(xticks=[], yticks=[], xlabel=faces.target_names[faces.target[i]])\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 5.7.4支持向量机总结  \n",
    "支持向量机是一种强大的分类方法，主要有四点理由：  \n",
    "• 模型依赖的支持向量比较少，说明它们都是非常精致的模型，消耗内存少。  \n",
    "• 一旦模型训练完成，预测阶段的速度非常快。  \n",
    "• 由于模型只受边界线附近的点的影响，因此它们对于高维数据的学习效果非常好——即\n",
    "使训练比样本维度还高的数据也没有问题，而这是其他算法难以企及的。  \n",
    "• 与核函数方法的配合极具通用性，能够适用不同类型的数据。  \n",
    "  \n",
    "但是，SVM 模型也有一些缺点：  \n",
    "• 随着样本量 N 的不断增加，最差的训练时间复杂度会达到$O[N^3]$；经过高效处理后，也\n",
    "只能达到$O[N^2]$。因此，大样本学习的计算成本会非常高。  \n",
    "• 训练效果非常依赖于边界软化参数$C$的选择是否合理。这需要通过交叉检验自行搜索；\n",
    "当数据集较大时，计算量也非常大。  \n",
    "• 预测结果不能直接进行概率解释。这一点可以通过内部交叉检验进行评估（具体请参见\n",
    "SVC 的 probability 参数的定义），但是评估过程的计算量也很大。"
   ]
  }
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